[Arkhaine_kupo]

> they’re using their equity to buy compute that is critical to improving their core technology

But we know that growth in the models is not exponential, its much closer to logarithmic. So they spend =equity to get >results.

The ad spend was a merry go round, this is a flywheel where the turning grinds its gears until its a smooth burr. The math of the rising stock prices only begins to make sense if there is a possible breakthrough that changes the flywheel into a rocket, but as it stands its running a lemonade stand where you reinvest profits into lemons that give out less juice

[J_McQuade]

There is something about an argument made almost entirely out of metaphors that amuses me to the point of not being able to take it seriously, even if I actually agree with it.

[powerhouse007]

As much as I dislike metaphors, this sounded reasonable to me. Just don't go poking holes in the metaphor instead of the real argument.

[DenisM]

OpenAI invests heavily into integration with other products. If model development stalls they just need to be not worse than other stalled models while taking advantage of brand recognition and momentum to stay ahead in other areas.

In that sense it makes sense to keep spending billions even f model development is nearing diminishing return - it forces competition to do the same and in that game victory belongs to the guy with deeper pockets.

Investors know that, too. A lot of startup business is a popularity contents - number one is more attractive for the sheer fact of being number one. If you’re a very rational investor and don’t believe in the product you still have to play this game because others are playing it, making it true. The vortex will not stop unless limited partners start pushing back.

[otherjason]

But, if model development stalls, and everyone else is stalled as well, then what happens to turn the current wildly-unprofitable industry into something that "it makes sense to keep spending billions" on?

[chii]

The bigger threat is if their models "stall", while a new up-start discovers an even better model/training method.

What _could_ prevent this from happening is the lack of available data today - everybody and their dog is trying to keep crawlers off, or make sure their data is no longer "safe"/"easy" to be used to train with.

[sidewndr46]

There's at least one contributor here on HN that believes growth in models is strictly exponential: https://www.julian.ac/blog/2025/09/27/failing-to-understand-...

[brokencode]

Yeah, except you can keep on squeezing these lemons for a long time before they run out of juice.

Even if the model training part becomes less worthwhile, you can still use the data centers for serving API calls from customers.

The models are already useful for many applications, and they are being integrated into more business and consumer products every day.

Adoption is what will turn the flywheel into a rocket.

[mentalgear]

Well, the thing is that that kind of hardware chips quickly decrease in value. It's not like the billions spend in past bubbles like the 2000s where internet infrastructure was build (copper, fibre) or even during 1950s where transport infrastructure (roads) were build.